Understanding the spatiotemporal evolution of runoff and its driving mechanisms is of great significance for water resources development, utilization, and sustainable management in mid- to high-latitude river basins under climate change. However, runoff variability is jointly influenced by multiple meteorological factors, and a comprehensive understanding of its multi-scale response characteristics and the relative contributions of different drivers remains limited. In this study, runoff data from three hydrological stations in the Songhua River Basin during 1980–2022 were analyzed. A set of statistical and time-series methods, including the Mann–Kendall test, Pettitt change-point test, Hurst exponent, wavelet analysis, and wavelet coherence, was applied, and a random forest model was used to quantify the influence of key climatic factors such as precipitation, air temperature, and evapotranspiration. The results show that air temperature exhibits significant increasing trends in all four seasons, with the strongest warming occurring in spring (Sen’s slope ≈ 0.06 °C a⁻¹). Precipitation displays pronounced spatial heterogeneity and interannual variability, while evapotranspiration shows an overall increasing trend. Both runoff and major meteorological variables exhibit significant spatial heterogeneity across the basin. Hydro-meteorological variables also show distinct periodic variations among seasons, with temperature, precipitation, and evapotranspiration exhibiting stronger seasonal fluctuations during summer. Wavelet coherence analysis indicates that short-term runoff variability is mainly driven by temperature and precipitation. Temperature exhibits significant coherence with runoff across multiple time scales ranging from approximately 2 to 20 years, whereas precipitation shows stronger coherence at medium- to long-term scales (approximately 10–35 years), with evident seasonal differences. Random forest results indicate that evapotranspiration is the most important contributor to runoff variability at all three stations, accounting for 33.5%, 28.6%, and 26.2% of the total importance at Jiamusi, Fuyu, and Jiangqiao stations, respectively. Temperature and sunshine duration rank second, while precipitation and relative humidity contribute comparatively less. These findings indicate that evapotranspiration plays a key regulatory role in long-term water balance. In addition, runoff exhibits multi-scale variability and a transition from gradual changes to stage-like abrupt shifts. The findings provide a scientific basis for water resources management, flood mitigation, and climate change adaptation in the Songhua River Basin.

The water supply–demand pattern directly affects the stability of ecosystems and the level of agricultural sustainable development. Based on the theoretical framework of water supply–demand ecosystem services, this study took 1990–2020 as the research period, and integrated the water yield module of the InVEST model, urban stormwater retention module, standard deviational ellipse method, and spatial interpolation to accurately calculate regional water yield, water demand, water supply–demand ratio, and gravity center shift. The aim was to reveal the spatiotemporal evolution and mechanism of water budget in the Sanjiang Plain under large-scale agricultural expansion. The results showed the following: (1) From 1990 to 2020, the annual water yield in the Sanjiang Plain fluctuated from 12.16 billion m³/yr to 23.28 billion m³/yr, with a spatial pattern of “high in the northwest and southeast, low in the central Songhua River”, and high-value areas were highly coincident with natural ecological land. (2) The multi-year average water demand was approximately 13.0 billion m³/yr, experiencing three phases, “slow growth–rapid rise–stable slowdown”, showing a distribution characteristic of “concentrated along rivers and high in urban areas”. (3) Water surplus and deficit areas maintained a rigid characteristic of “highland supply, lowland consumption” for a long time. The gravity center of deficit areas rotated counterclockwise from 132.35° E, 46.55° N to 132.58° E, 46.78° N, with an annual migration rate of 10–15 km/a. The gravity center of surplus areas rotated clockwise from 133.22° E, 47.45° N to 132.85° E, 46.92° N, with an annual migration rate of 15–20 km/a. (4) From 1990 to 2015, the regional water supply–demand pattern remained relatively stable. However, a pronounced transition occurred during 2015–2020, when surplus areas sharply decreased from 49% to 27%, while conflict zones expanded rapidly, indicating an evident deterioration of regional water security.

Vegetation dynamics are key indicators of climate change and ecological risk in cold regions. This study investigated vegetation–climate interactions in Heilongjiang Province, China, using an integrated analytical framework that combined the Mann–Kendall test, Pettitt test, Hurst exponent, Continuous Wavelet Transform, and Wavelet Transform Coherence with the Normalized Difference Vegetation Index and meteorological data from 1990 to 2024. The dominant vegetation comprises cold–temperate coniferous forests, mixed broadleaf–conifer forests, meadow steppes, and croplands. Results showed that (1) the climate system experienced asynchronous abrupt changes, with potential evapotranspiration, precipitation, and temperature changing in 2008, 2011, and 2013, respectively. Accordingly, the Normalized Difference Vegetation Index reversed in 2011, with greening rates in the range of 0.009–0.022 yr⁻¹ in the northwestern and central regions and browning rates in the range of −0.010 to −0.025 yr⁻¹ in the southwestern and eastern regions. (2) The Hurst exponent ranged from 0.23 to 0.48 for temperature and potential evapotranspiration, indicating strong anti-persistence and high future ecological vulnerability. (3) Wavelet coherence analysis identified precipitation as the dominant climatic driver at 6–9-year scales, whereas temperature shifted from a short-term positive driver to a long-term stressor, and potential evapotranspiration mainly regulated vegetation at 5–8-year scales. These findings provide scientific support for ecological risk assessment and climate-resilient cold regions engineering.